Computational Neuroscience

Computational Neuroscience
Course Number(s): 
MCB 131


  • Spring 2017

Course Level: 

  • Graduate, Undergraduate
Class Days/Times: 
Mon 2:30pm to 4:00pm
Wed 2:30pm to 4:00pm
Course Description: 

Follows trends in modern brain theory, focusing on local neuronal circuits and deep architectures. Explores the relation between network structure, dynamics, and function. Introduces tools from information theory, dynamical systems, statistics, and learning theory in the study of experience-dependent neural computation. Specific topics include: computational principles of early sensory systems; unsupervised, supervised and reinforcement learning; attractor computation and memory in recurrent cortical circuits; noise, chaos, and coding in neuronal systems; learning and computation in deep networks in the brain and in AI systems. Cross-listed in Physics and SEAS.